Safarnama @mindfullofASH

Documenting the headwinds I now see for AI. It won't seem like it, but I love AI and am long-term positive. But when "math doesn't math" I take note. 1. The core thesis for foundation model lab investment has been high upfront investment made worthwhile by significant long-term profits. 2. These are capital intensive businesses and the compute commitments are very high relative to revenue and require strong growth over long time periods. The "leverage" (commitments versus revenue) is extremely high. 3. The fundamentals are not as positive as they previously were: • Input costs are higher (commodities, chips, power) • Interest rates are higher • Competition is more intense • Scaling Laws are now problematic: exponential costs/power cannot continue 4. Forecasting compute spend is challenging and high risk due to (a) revenue uncertainty and (b) algorithm uncertainty 5. Revenue growth appears to be slowing. The technology is valuable, but ROI is proving to be more expensive and take longer than anticipated. 6. The future is likely "different models for different use cases" with the lower end of the market being highly competitive. 7. Core use cases such as agentic software engineering are likely to need approaches beyond next-token prediction. They are Σ₂ᴾ complexity problems requiring multi-objective optimization and likely a combination of Transformers and other methods. 8. Current forecasts in memory makers are built largely on quadratic attention. That will not persist: we are already seeing work from DeepSeek, Minimax and Nvidia that can cut RAM needs by 80% or more. 9. This means semiconductor valuations are substantially overinflated and will go through the traditional glut versus shortage cycle. 10. For foundation model providers: lower costs with competitive differentiation is good. However, lower costs with a lack of differentiation would mean lower revenues. This makes it harder to (a) service commitments and (b) pay back investors. 11. Leverage is substantially higher than in previous cycles, evidenced by leveraged ETFs, call option activity and margin loans. Korea is particularly susceptible. 12. 0DTE options create a profile that has stronger parallels to portfolio insurance and 1987 than any other point I can remember. 13. The combination of exponential increases in call activity coupled with the ties of semiconductors to structured products means there is a non-trivial systemic risk to the financial system. 14. Implied earnings growth rates are inconsistent with other periods in history. 15. Macroeconomically we cannot and should not fund exponential cost increases. History has shown us repeatedly that there are better ways (see Quick Sort and Simplex). 16. Significant supply is hitting the market via IPOs. –– Taken together: costs and competition are increasing while revenue growth is likely slowing. Valuations are fragile and prone to technology disruptions that are already here. Systemic financial market risk is extremely high.

1/6 Financial Times: "A company-level OECD analysis of government subsidies across 15 key industrial sectors found that nearly 60 per cent of Chinese firms’ global market share gains since 2005 could be attributed to subsidies." ft.com/content/885ca6…

🚨Michael Burry just said Elon Musk and Nvidia's deal is built on fake numbers. Burry published a detailed breakdown calling the entire structure "Fugazi", his word for fake. He is alleging that billions of dollars in Nvidia chips are being hidden off balance sheets, and that American retirees are unknowingly funding the whole thing. Nvidia, the world's largest AI chip company sold $5.4 billion worth of its most advanced GPUs, the GB200, to a company called Valor. Valor is not a real operating business. It is a special purpose vehicle, a shell company created specifically to hold these chips and nothing else. Nvidia also invested $1.9 billion of its own money directly into Valor on top of the sale. Those 100,000+ chips are now physically inside xAI's data center. xAI is Elon Musk's artificial intelligence company, the one that builds Grok. xAI is using every single one of those chips right now to run its AI models. But here is what Burry is flagging. Neither Nvidia nor xAI owns those chips on paper. Valor, the shell company holds legal title. That means $5.4 billion in GPU assets do not show up on Nvidia's balance sheet as inventory. They do not show up on xAI's balance sheet as assets. They are legally invisible to both companies. Nvidia gets to book the $5.4 billion as a completed sale and record it as revenue. xAI gets full use of the chips without owning them. And the risk disappears into a shell company in the middle. Now here is where American retirees enter the picture. Valor needed $3.5 billion in debt to fund this structure. Apollo provided it. Apollo is one of the largest asset managers on earth with $1.03 trillion under management and $834 billion specifically in private credit. Apollo raised the $3.5 billion, packaged it into debt securities, and sold those securities to Athene. Athene is Apollo's own insurance company. It sells fixed and indexed annuities, retirement savings products, to ordinary Americans. When a retiree buys an Athene annuity, they believe their money is sitting in safe, stable investments. That money is now inside a structure funding Elon Musk's AI data center. The numbers inside Athene are most alarming. Athene holds $74.2 billion in reserves. It has moved $217 billion in assets into a captive insurer based in Bermuda, meaning those assets sit outside normal US insurance regulation and oversight. Of the entire portfolio, 34.7%, equal to $103 billion, is classified as Level 3 assets. Level 3 is an accounting classification that means there is no observable market price for these assets. No outside party can independently verify what they are actually worth. The leverage sitting on top of those unpriced assets is 16 times. Burry's says: Every step of this structure is technically legal and publicly disclosed. But the entire thing was deliberately engineered across 8 to 12 steps to move credit risk off balance sheets and away from any market pricing. - Nvidia books the revenue. - Apollo collects the fees. - xAI gets the computing power. - And retirees sitting at the bottom of a 16x leveraged Bermuda insurance structure, holding $103 billion in assets with no market price carry the risk without knowing it exists.

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Was in Baroda yesterday Earlier in Ahmedabad Prior to that Chennai The manufacturing revolution The green energy revolution The logistics revolution This is all for real World class manufacturing facilities by hitherto unknown founders US / global accreditions Global manufacturing sourcing from India Does not matter what the external environment is Growing strongly and methodically Remarkable focus on automation It’s v v different from looking at the exchange screens the whole day and deriving conclusions on the economy from that The INR depreciation is the best thing that’s happened Exports become more competitive Nominal growth accelerates Outsourcing from India gets turbo charged As scale rises , backward integration accelerates Focus on new technologies goes up . We saw a hydrogen electrolyser powered green galvanising plant yesterday .. all being done at “Indian costs” The new breed of entrepreneurs are actually not new .. they have been at it for 20-30 years and finally now getting the platform to grow aggressively 👆from a @PineTreeMacro investor.

The biggest transformation in modern warfare for the US military isn’t happening in drones, as important as they are. It’s happening in space.

Tyler Rogoway @Aviation_Intel

5 days ago

Pentagon’s Plans To Track Aircraft From Orbit Accelerated With New $4B SpaceX Deal A satellite network to track aircraft could offer unprecedented ability to surveil the skies globally and make AEW&C aircraft redundant. twz.com/space/pentagon…

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When asked about India's contributions to science, many go back thousands of years and speak about zero, ancient universities, metallurgy, or Ayurveda. While those achievements are undoubtedly important, India's contributions to modern science are equally remarkable and far less appreciated. What is fascinating is that many of these contributions emerged during the late nineteenth and early twentieth centuries, when India was under colonial rule and had very limited scientific infrastructure. Consider a few examples. Jagadish Chandra Bose demonstrated millimetre wave wireless communication and developed one of the earliest semiconductor detectors. More than a century later, millimetre waves are at the heart of modern 5G and future 6G communication systems, while semiconductor devices underpin the entire digital economy. Meghnad Saha developed the ionization equation, which made it possible to determine the temperature and chemical composition of stars from their spectra. Modern astrophysics, stellar evolution studies, plasma science, and even fusion research continue to rely on principles that emerged from his work. Satyendra Nath Bose developed the statistical framework governing an entire class of particles now known as bosons. Lasers, superconductors, Bose-Einstein condensates, photonic technologies, and many emerging quantum computing platforms trace their origins to ideas he developed in a few pages written in Dhaka. Upendranath Brahmachari developed Urea Stibamine, one of the first successful targeted treatments for a major infectious disease. Beyond saving countless lives from Kala Azar, his work helped establish the foundations of modern antiparasitic chemotherapy and drug discovery for neglected tropical diseases. Prafulla Chandra Ray's contribution went far beyond discovering new chemical compounds. He demonstrated that scientific research could be translated into indigenous industrial capability. Through Bengal Chemicals, he laid some of the earliest foundations for science-driven manufacturing in India, a legacy that can be seen today in India's globally significant pharmaceutical industry. These were not incremental advances. These were foundational contributions that shaped entire scientific disciplines and industries worth trillions of dollars today. These foundational works also helped many receive Nobel Prizes later. India has never lacked talent. Even under colonial rule, Indian scientists produced ideas that changed the world. The challenge now is to build institutions that trust scientists, reduce bureaucratic friction, invest consistently in research, protect academic freedom, and create environments where transformative ideas can flourish. If a colonized nation with limited resources could produce all this great science, one can only imagine what India can achieve if we fully unleash the talent that exists today. We just need to empower 'real' scientists. youtu.be/RXMHA6T_KP8?si…

"Appa, why should I study Maths" "It's God's chosen language. It's the form that truth takes. It's the code to unlocking nature's mysteries. It's the bedrock of all science & engineering. Last but not least, it's why Jane Street earns $40 billion with 3000 employees"

C. S. Lewis said it best you need to read old books

Day @Daywrotethis

a week ago

A man who reads old books cannot be fully captured by modern stupidity. He has dead kings, prophets, poets, killers, saints, drunks, generals, and madmen whispering in his bloodstream. The feed has no chance against this.

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What do policy enthusiasts, civilizational thinkers, governance practitioners, and public leaders all have in common? They read seriously. Not just to stay informed, but to understand how states function, how institutions evolve, how societies sustain themselves, and how ideas shape civilizations across generations. Introducing 'Policy in Pages' by Nītividhāna. A carefully curated collection of essential reads on governance, public leadership, statecraft, institutions, culture, and civilization. @mmuthukrishna @vijaykelkar @ajay_shah

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How AI will change media: “The value of polish is going to go down and the value of personal charisma, style, and weirdness is going to go up,” says @jasminewsun

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C. S. Lewis with maybe the most important paragraph ever written for people with creative ambitions

Worthy to listen/read Interview of Rajiv Kumar,former Vice Chairman of NITI Aayog on India's underperformance in manufacturing on output-to-GDP metric & anemia plaguing India's labour-intensive export sectors like RMG. thecore.in/podcasts/india…

Insane idea from Opus 4.8

i just can't unsee this now ..

*Walter Bloomberg @DeItaone

a week ago

CHINA IS RESTRICTING OVERSEAS TRAVEL FOR TOP AI PROFESSIONALS IN PRIVATE FIRMS SUCH AS ALIBABA GROUP HOLDING AND DEEPSEEK

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If you are not lucky; then you have a shot at becoming unbreakable.

Michael Hartnett, BofA | This Is The Biggest Bubble Since The Railroads The current AI-driven market rally resembles one of the biggest speculative bubbles in history, comparable to the Nifty Fifty or dot-com era, but investors are unlikely to aggressively sell before two catalysts occur: a major OpenAI/SpaceX-style IPO cycle and a clear Fed policy tightening shock tied to rising CPI from tariffs and inflation. - AI mega-caps now dominate market concentration, with the “AI bubble” larger than past railroad, Japan, and dot-com bubbles by some metrics. - Bond yields are the main warning signal: the rise in long-term yields and global cost of capital seen as dangerous for risk assets, especially leveraged consumers, private equity, housing, and emerging markets. - Several macro stress signals are flashing: weak Asian currencies (KRW, JPY, INR, IDR), widening high-yield spreads, EM outflows, and BofA’s Bull & Bear Indicator hitting a contrarian “sell” level. - Historically, speculative IPO waves (Alibaba, NTT, Visa, etc.) often marked medium-term market tops rather than immediate crashes. - Current gains are very narrow (“wealth effect, not wage effect”), while equal-weight consumer stocks remain weak versus the S&P 500. - Despite near-term bubble concerns, structurally bullish on emerging markets and commodities. - Preferred post-bubble opportunities would be consumer stocks and smaller AI adopters/disruptors rather than dominant mega-cap AI platforms. - Geopolitics, energy, AI competition with China, and inflation are increasingly interconnected themes shaping markets. Zeitgeist quote: "Everyone is now convinced that equities are the best inflation hedge." Feedback from recent London trip, main soundbites: - "we’re long and paranoid,” - “wants/needs to de-escalate Iran, and stocks pop, yields drop on deal,” - “if UK gilts find love, everything finds love,” - “European electorate shifting decisively right, Farage in UK, Le Pen in France, and you watch the AfD in Germany will win Saxony-Anhalt in September, their first state election,” - “the fear in bonds is nowhere near as strong as greed in equities,” - “Warsh will be rhetorically hawkish but practically dovish over the summer,” - “US actions in Venezuela, Ukraine, Iran, Greenland, Cuba should be viewed through single strategic lens competition with China in AI, which can only be won by securing access to critical resources.” ( h/t Octavian Adrian Tanase)

India’s "Hidden Urbanisation" (EAC-PM Working Paper) (PM EAC has come up with an excellent paper on India's "hidden urbanisation" and the challenges it poses for policy makers, often leading to misallocation of resources. Although the paper is quite analysis heavy, having being authored by economists of repute, I have tried to summarize key points here. Must read for any serious aspirant!!) 1. The Core Challenge: Official data (2011 Census) places India at 31.1% urban. However, satellite data tracking reveals India was already 63% urban by 2015. Legacy census definitions (requiring 75% male non-agricultural employment) fail to capture actual economic shifts, leaving thousands of structurally urban "Census Towns" misclassified and governed as rural panchayats. 2. India’s cities generate over 60% of GDP on 3% of land, yet a 1% increase in urbanisation yields only a 1.7% rise in GDP per capita (compared to 3.9% globally) due to severe structural and governance inefficiencies. 3. Objective Redefinition & Funding Triggers: There's a need to move away from outdated census metrics to real-time satellite data measuring Built-Up Volume as the primary proxy for urbanisation. Paper argues the need to Implement automated administrative mechanisms to instantly upgrade rural settlements to "urban" status once they clear satellite-derived thresholds. 4. Formulaic Funding: Devolve central funds equitably (1/3rd weightage each) based on three objective satellite metrics: Total Built-Up Volume, Growth in Built-Up Volume, and Built-Up Volume per Population. 5. Accountability & Governance Reforms A. Shift bureaucratic evaluations away from spending inputs toward five tangible citizen-centric outcomes: Residential Life, Transportation, Public Safety, Greening, and Public Health B. Skin in the Game:Grant urban bureaucrats a minimum fixed tenure of 3 years to prevent political churning, and introduce variable pay tied directly to local economic growth and the five outcome domains. C. Enforced Democracy: Make central urban funding explicitly conditional on states conducting timely Urban Local Body (ULB) elections, curbing the current trend of long-delayed local polls. D. Internal Revenue: Modernize municipal property taxes, rationalise user fees, and optimize public land leasing. E. Pooled Municipal Bonds: Establish State-level Special Purpose Financial Entities (SPFEs) to pool bond issuances, allowing smaller cities access to capital markets. F. Outcome Funds: Deploy Union-level Outcome Funds and Social Impact Bonds where private investors finance urban projects, and public funds are disbursed only after independent third parties verify successful delivery. 6. Policy Conclusion: India must pivot from a reactive, welfare-centric rural focus to a proactive, growth-centric urban strategy—governing cities based on real-time spatial data, holding administrators financially accountable for liveability outcomes, and unlocking land markets to build a "Viksit Bharat" #upsc #upsc2026 #hiddenurbanisation #upsceconomics @ShamikaRavi

Prof. Shamika Ravi @ShamikaRavi

2 weeks ago

'Hidden Urbanisation' is one of India's biggest policy concerns today. Census data Vs. Satellite data tells a compelling story. For e.g. proportion of districts with >50% rural population - Census: 87% Satellite: 17% Read the full paper here: eacpm.gov.in/wp-content/upl…

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your problem is that you don’t care enough.

खैनी स्वीडिश वे ऑफ लाइफ़ है।

Polymarket @Polymarket

2 weeks ago

JUST IN: Sweden says France’s nicotine pouch ban is an “attack on the Swedish way of life”

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🚨 THE ENTIRE AI BOOM MIGHT BE BUILT ON FAKE REVENUE. Latest corporate filings show that OpenAI and Anthropic alone make up over half of the entire $2 trillion future cloud backlog held by Microsoft, Oracle, Google, and Amazon. This massive pipeline is actually being created through a circular accounting trick called a round trip revenue loop. But how it works ? A tech giant gives billions of dollars to an AI startup as an "investment". But hidden in the contract is a strict rule forcing the startup to hand that exact same money straight back to the tech giant to rent their computer servers. Look at the documented case of Microsoft and OpenAI. When Microsoft invested $13 billion into OpenAI, it didn't just give them cash; it gave them "cloud credits" to use Microsoft servers. OpenAI used those exact credits to train its AI models, and Microsoft then turned around and recorded that server usage as brand new "cloud revenue" from a customer. The tech giant is literally paying itself with its own money and calling it a sale. This is why OpenAI’s annual cloud bill has ballooned to over $60 billion, double its actual revenue of $25 billion, kept alive solely by this recycled funding loop. Anthropic runs the exact same play, spending $2.66 billion on Amazon Web Services in just nine months, which was basically 100% of all the money it earned at the time. This manufactured demand triggers a second accounting trick where tech giants book massive paper profits. Every time a startup gets a higher value from a new funding round, the tech giant updates the value of its investment on its books and counts that unearned paper gain as direct profit. In Q1 2026, Alphabet reported a record $62.6 billion profit, but $28.7 billion nearly half, was just a paper markup on its Anthropic investment. In the same quarter, Amazon reported $30.3 billion in profit, but $16.8 billion of it was just an Anthropic paper gain. While Amazon reported record profits, its actual free cash flow collapsed 95% to just $1.2 billion because it had to spend $44.2 billion in real cash to build physical data centers. This has created a massive danger where these giant companies rely heavily on just one or two unstable startups. Microsoft has 49% of its $627 billion future backlog tied to OpenAI, while Oracle has an incredible 54% of its entire $553 billion pipeline relying on OpenAI alone. This perfectly mirrors the 2001 dot-com crash when Global Crossing and Qwest Communications swapped identical fiber-optic network capacity with each other just to book fake sales. Qwest had to erase $1.4 billion in fake income, and Global Crossing went completely bankrupt. The only difference is that the dot-com swaps were illegal, but today's AI loop is fully legal under current accounting rules. This legal loop inflates tech company stock prices, forcing automatic retirement accounts and index funds to buy even more of these tech stocks. It is a self feeding loop where investments, sales, and stock prices all go up on paper without the AI technology ever making real cash profits.

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